Title: Automatic detection of learning styles based on dynamic Bayesian network in adaptive e-learning system

Authors: Lamia Mahnane; Mohamed Hafidi

Addresses: LRS Laboratory, Department of Computer Science, University of Badji Mokhtar, P.O. Box 12, 23000, Annaba, Algeria ' LRS Laboratory, Department of Computer Science, University of Badji Mokhtar, P.O. Box 12, 23000, Annaba, Algeria

Abstract: A large number of studies attest that learning is facilitated if the teaching strategies are in accordance with the students learning styles (LS), making the learning process more effective and considerably improving student's performances. But, traditional approaches for detection of LS are inefficient. This work determines the current preferences through dynamic Bayesian network that represent the matches between LS and teaching strategies in order to determine how much a given strategy is interesting to a student. The LS theory that supports this approach is the LS model proposed by Felder-Silverman's learning styles model (FSLSM). Our approach gradually and constantly adjusts the student model, taking into account students' performances, student's effort, student's intensity, student's resistance and student's attention. Promising results were obtained from experiments, and some of them are discussed in this paper.

Keywords: dynamic Bayesian networks; learning styles; teaching strategy; adaptive e-learning; online learning; electronic learning; student performance; automatic detection; Felder-Silverman.

DOI: 10.1504/IJIL.2016.079067

International Journal of Innovation and Learning, 2016 Vol.20 No.3, pp.289 - 308

Received: 10 Jan 2015
Accepted: 13 Apr 2015
Published online: 12 Sep 2016 *

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